Soil And Water Assessment Tool Model Performance Research Articles

Natural streamflow reconstruction and quantification of hydrological drought in the Soan River basin, Pakistan

AbstractClimate change and rapid socioeconomic development have exacerbated the damage caused by hydrological droughts. To ensure effective drought defense and infrastructure development, it is essential to investigate variations in hydrological droughts. The primary objective of this study is to reconstruct the natural streamflow by using Soil and Water Assessment Tool (SWAT) hydrological modeling. The hydrological drought at different time scales (1, 3, 6, and 12 months) were measured using the streamflow drought index (SDI). The statistical parameters, including Nash–Sutcliffe Efficiency and the Coefficient of Determination, which yielded values of 0.84 and 0.82 during the calibration period and 0.78 and 0.76 during the validation period, respectively, showed a satisfactory SWAT model performance. Additionally, the Pettit test was used to identify a change point in streamflow within the 1991–2015 timeframe, leading to the division of the study period into two distinct phases: an undisturbed period (1991–1998) and a disturbed period (1999–2015). The SDI index‐based analysis revealed 9.39% moderate drought and 3.13% severe drought during the undisturbed period, while 11.76% moderate drought and 7.35% severe drought may happen due to the human influences that occurred in the disturbed period. These findings enhance the understanding of the hydrological drought variations in the Soan River basin for optimizing the water resources management system and effectively preventing and mitigating drought‐related damages.

Comparison of the performance of SWAT and hybrid M5P tree models in rainfall-runoff simulation.

Stream flow forecasting is a crucial aspect of hydrology and water resource management. This study explores stream flow forecasting using two distinct models: the Soil and Water Assessment Tool (SWAT) and a hybrid M5P model tree. The research specifically targets the daily stream flow predictions at the MH Halli gauge stations, located along the Hemvati River in Karnataka, India. A 14-year dataset spanning from 2003 to 2017 is divided into two subsets for model calibration and validation. The SWAT model's performance is evaluated by comparing its predictions to observed stream flow data. Residual time series values resulting from this comparison are then resolved using the M5P model tree. The findings reveal that the hybrid M5P tree model surpasses the SWAT model in terms of various evaluation metrics, including root-mean-square error, coefficient of determination (R2), Nash-Sutcliffe efficiency, and degree of agreement (d) for the MH Halli stations. In conclusion, this study shows the effectiveness of the hybrid M5P tree model in stream flow forecasting. The research contributes valuable insights into improved water resource management and underscores the importance of selecting appropriate models based on their performance and suitability for specific hydrological forecasting tasks.

SWAT Model Performance Using Spatially Distributed Saturated Hydraulic Conductivity (Ksat) and Varying-Resolution DEMs

Saturated hydraulic conductivity (Ksat) is a hydrologic flux parameter commonly used to determine water movement through the saturated soil zone. Understanding the influences of land-use-specific Ksat on the model estimation error of water balance components is necessary to advance model predictive certainties and land management practices. An exploratory modeling approach was developed in the physically based Soil and Water Assessment Tool (SWAT) framework to investigate the effects of spatially distributed observed Ksat on local water balance components using three digital elevation model (DEM) resolution scenarios (30 m, 10 m, and 1 m). All three DEM scenarios showed satisfactory model performance during calibration (R2 > 0.74, NSE > 0.72, and PBIAS ≤ ±13%) and validation (R2 > 0.71, NSE > 0.70, and PBIAS ≤ ±6%). Results showed that the 1 m DEM scenario provided more realistic streamflow results (0.315 m3/s) relative to the observed streamflow (0.292 m3/s). Uncertainty analysis indicated that observed Ksat forcings and DEM resolution significantly influence predictions of lateral flow, groundwater flow, and percolation flow. Specifically, the observed Ksat has a more significant impact on model predictive confidence than DEM resolution. Results emphasize the potential uncertainty of using observed Ksat for hydrological modeling and demonstrate the importance of finer-resolution spatial data (i.e., 1 m DEM) applied in smaller watersheds.

Impact of Climate Change on the Water Balance of the Akaki Catchment

Climate change has an impact on water resources. Estimations of the variations in water balance under climate change variables are essential for managing and developing the water resource of a catchment. The current investigation evaluated the magnitude of the change in the water balance component of the Akaki catchment, Ethiopia, using the semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT), with the integration of the Coordinated Regional Downscaling Experiment of Africa under RCP4.5 and 8.5. The SWAT model was developed using spatial and temporal data; it was calibrated (1991–2001) and validated (2002–2004) using SWAT-CUP. The statistical monthly SWAT model performance values of the NSE, PBIAS (%), and R2 showed good agreement between calibration and validation. On an annual basis, projected rainfall is expected to increase by 14.96%, 4.13%, 8.39%, and 10.39% in the 2040s under RCP4.5 and 8.5 and in the 2060s under RCP4.5 and 8.5, respectively, with inconsistent change on a monthly projections basis for each scenario. The projected monthly and yearly temperatures are expected to increase under different climate change scenarios. Annual evapotranspiration and potential evapotranspiration increased under both RCPs, whereas surface runoff, lateral flow, and water yield declined under the climate scenarios of each RCP. Monthly projected water yield showed a non-uniform change in the first 30 years and in the second years under the RCP4.5 and RCP8.5 scenarios. These results show that the catchment is highly vulnerable to hydrological and agricultural drought due to water availability. These research findings provide valuable evidence on the role of climate change in water balance, which will help decision makers to achieve better water resource management.

Intercomparison of SWAT and ANN techniques in simulating streamflows in the Astore Basin of the Upper Indus.

The current research work was carried out to simulate monthly streamflow historical record using Soil and Water Assessment Tool (SWAT) and Artificial Neural Network (ANN) at the Astore Basin, Gilgit-Baltistan, Pakistan. The performance of SWAT and ANN models was assessed during calibration (1985-2005) and validation (2006-2010) periods via statistical indicators such as coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), percent bias (PBIAS), and root-mean-square error (RMSE). R2, NSE, PBIAS, and RMSE values for SWAT (ANN with Architecture (2,27,1)) models during calibration are 0.80 (0.88), 0.73 (0.82), 15.7 (0.008), and 79.81 (70.34), respectively, while during validation, the corresponding values are 0.71 (0.86), 0.66 (0.95), 17.3 (0.10), and 106.26 (75.92). The results implied that the ANN model is superior to the SWAT model based on the statistical performance indicators. The SWAT results demonstrated an underestimation of the high flow and overestimation of the low flow. Comparatively, the ANN model performed very well in estimating the general and extreme flow conditions. The findings of this research highlighted its potential as a valuable tool for accurate streamflow forecasting and decision-making. The current study recommends that additional machine learning models may be compared with the SWAT model output to improve monthly streamflow predictions in the Astore Basin.

Potential assessment of calibration approaches using the SWAT hydrological model for streamflow and sediment yield for a large-scale catchment

ABSTRACT This study explored the potential of three calibration approaches – single-site, multisite, and multisite–multivariable – to simulate the streamflow and sediment yield of the Upper Blue Nile (UBN) river basin using the Soil and Water Assessment Tool (SWAT). Several statistical indices were used to verify the SWAT model performance. While all three approaches reasonably estimate the streamflow and sediment yield, the multisite calibration approach was found to perform better in handling the spatial variability of parameters and their influence on streamflow and sediment yield over the entire catchment. Further, evaluations of hydropower potential at the selected location were conducted to demonstrate the importance of calibration approaches in planning and designing various water resource structures.

A comparison of the performance of SWAT and artificial intelligence models for monthly rainfall–runoff analysis in the Peddavagu River Basin, India

Abstract Rainfall–runoff (R–R) analysis is essential for sustainable water resource management. In the present study focusing on the Peddavagu River Basin, various modelling approaches were explored, including the widely used Soil and Water Assessment Tool (SWAT) model, as well as seven artificial intelligence (AI) models. The AI models consisted of seven data-driven models, namely support vector regression, artificial neural network, multiple linear regression, Extreme Gradient Boosting (XGBoost) regression, k-nearest neighbour regression, and random forest regression, along with one deep learning model called long short-term memory (LSTM). To evaluate the performance of these models, a calibration period from 1990 to 2005 and a validation period from 2006 to 2010 were considered. The evaluation metrics used were R2 (coefficient of determination) and NSE (Nash–Sutcliffe Efficiency). The study's findings revealed that all eight models yielded generally acceptable results for modelling the R–R process in the Peddavagu River Basin. Specifically, the LSTM demonstrated very good performance in simulating R–R during both the calibration period (R2 is 0.88 and NSE is 0.88) and the validation period (R2 is 0.88 and NSE is 0.85). In conclusion, the study highlighted the growing trend of adopting AI techniques, particularly the LSTM model, for R–R analysis.

Pitfalls in hydrologic model calibration in a data scarce environment with a strong seasonality: experience from the Adyar catchment, India

Process-based hydrologic models can provide necessary information for water resources management. However, the reliability of hydrological models depends on the availability of appropriate input data and proper model calibration. In this study, we demonstrate that common calibration procedures that assume stationarity of hydrological processes can lead to unsatisfactory model performance in areas that experience a strong seasonal climate. Moreover, we develop a more robust calibration procedure for the Soil and Water Assessment Tool (SWAT) in the Adyar catchment of Chennai, India. Calibration was carried out based on seasonal decomposition and by successively shifting the calibration period. Daily and monthly streamflow records were used to investigate how these different calibration procedures influence model parameterization. Results show that SWAT model performance improved when calibrated after separating the streamflow into wet and dry seasons. The wet season calibration increased the Kling Gupta Efficiency coefficient and Nash–Sutcliffe Efficiency coefficient values from 0.56 to 0.68 and 0.19 to 0.51, respectively, compared to calibration based on wet and dry seasons together. In addition, when calibration time periods were shifted, resultant sets of model parameter values and performance metrics differed. Calibration based on the 2004–2009 period resulted in an overestimation of streamflow by 8.2%, whereas the overestimation was 12.1%, 18.3%, and 20.0% for the 2004–2010, 2004–2011, and 2004–2012 periods, respectively. This study underlines that both the availability of observed streamflow data and the way these data are applied to calibration have a strong impact on model parameterization and performance.

Assessing CFSR climate data for rainfall-runoff modeling over an ungauged basin between Iraq and Iran

In most developing countries, networks of gauge observations are frequently sparse and may have significant spatial and temporal gaps in the meteorological records. Reanalysis products are extensively used as alternative climate inputs for watershed modeling that provide continuous data worldwide. However, the direct utilization of such products is highly doubtful at different basin scales. This article aims to evaluate the accuracy of the Climate Forecast System Reanalysis (CFSR) product for streamflow simulation using the Soil and Water Assessment Tool (SWAT) model in the northeastern part of the Little Zab River Basin (LZRB), a mountainous basin between Iraq and Iran. The quality of CFSR was assessed from two viewpoints: (1) the precipitation aspect, an analysis of the CFSR precipitation compared to observational meteorological data (OMD) from 2004 to 2013 on a daily time scale, and (2) the hydrology aspect, a comparison of the SWAT model performance based on OMD (scenario I) and the CFSR product (scenario II) for monthly streamflow simulations. Results showed that the CFSR precipitation had a satisfactory correlation with rain gauge data ranging from 0.55 to 0.81. The performance of OMD in SWAT-based streamflow simulation achieved a ‘very good’ rating in calibration (2006–2010) and validation (2011–2013). Meanwhile, the CFSR-based model demonstrated a ‘very good’ hydrologic prediction skill in calibration. In contrast, CFSR revealed a lower performance in the validation period as it considerably overestimated base and peak flows in LZRB. Nevertheless, model uncertainty analysis after calibration showed that more than 88% of measured streamflow values at the basin outlet were bracketed within the 95 percent prediction uncertainty (95PPU) band for OMD and CFSR. These findings revealed that the CFSR product, after proper bias correction, could be a reliable alternative climate input for hydrologic modeling and climatological studies in semi-arid basins where gauge data is limited.

Simulation of Rainfall-Runoff process using SWAT model in Bouhamdane watershed, Algeria

The current research examines the runoff response in the Bouhamdane watershed in Algeria using the soil and water assessment tool (SWAT). The SWAT model is applied for the Bouhamane watershed, which includes three sub-watersheds and 45 Hydraulic Response Units (HRUs). To assess the ability and effectiveness of the model, one-gauge station in the basin (sabat) was chosen. Monthly discharge flow data are sourced from Algeria's National Water Resources Agency (NWRA). The soil and water assessment tool calibration uncertainty programs (SWAT-CUPs) with the sequential uncertainty fitting (SUFI 2) algorithm were used to calibrate and validate the model. The model was run from 1985 to 2004, with a calibration period between 1985 and 1994 and a validation period between 1995 and 2005. The model's runoff simulation efficiency has been improved by adjusting watershed input parameters. The SWAT model's performance was assessed statistically (coefficient of determination [R2], Nash-Sutcliffe Efficiency Coefficient [NSE], and Percent BIAS [PBIAS]). The monthly calibration R2, NSE, and PBIAS were 0.89, 0.68, and 43, respectively, and the monthly validation R2, NSE, and PBIAS were 0.78, 0.76, and 10.4, respectively. These results support that the SWAT model is an effective tool for simulating the surface runoff of the Bouhamdane watershed.

Informing the SWAT model with remote sensing detected vegetation phenology for improved modeling of ecohydrological processes

The Soil and Water Assessment Tool (SWAT) model has been widely applied for simulating the water cycle and quantifying the influence of climate change and anthropogenic activities on hydrological processes. A major uncertainty of SWAT stems from the poor representation of vegetation dynamics due to the use of a simplistic vegetation growth and development module. Using long-term remote sensing-based phenological data, the SWAT model’s vegetation module was improved by adding a dynamic growth start date and the dynamic heat requirement for vegetation growth rather than using constant values. The new SWAT model was verified in the Han River basin, China, and found its performance was much improved in comparison with that of the original SWAT model. Specifically, the accuracy of the leaf area index (LAI) simulation improved notably (coefficient of determination (R2) increased by 0.193, Nash–Sutcliffe Efficiency (NSE) increased by 0.846, and percent bias decreased by 42.18 %), and that of runoff simulation improved modestly (R2 increased by 0.05 and NSE was similar). Additionally, it is found that the original SWAT model substantially underestimated evapotranspiration (Penman-Monteith method) in comparison with the new SWAT model (65.09 mm (or 22.17 %) for forests, 92.27 mm (or 32 %) for orchards, and 96.16 mm (or 36.4 %) for farmland), primarily due to the inaccurate representation of LAI dynamics. Our results suggest that an accurate representation of phenological dates in the vegetation growth module is important for improving the SWAT model performance in terms of estimating terrestrial water and energy balance.

AN INTEGRATED STUDY USING GIS AND SWAT METHOD FOR RAINFALL RUNOFF MODELLING IN KULSI WATERSHED, ASSAM

The present study is based on the estimation of runoff using GIS and SWAT in the Kulsi Watershed, Assam. Soil and Water assessment tool (SWAT) is a physically based distributed parameter model which was developed to predict runoff, sediment, erosion from specific watersheds under different input conditions. For understanding the rainfall-runoff behavior of the basin, SWAT hydrological model was used and divided in 19 subwatersheds and 188 Hydrogical Response Units. The watershed comprises of four Land use classifications (River/water bodies, Open forest, dense forests and barren land) with three soil classes. The delineated area of the study is found to be 1754.39 km². The study indicates that the annual rainfall runoff relationship correlates with magnitude of total runoff. From the calibration output, the simulated stream flow hydrograph deviates slightly with the observed stream flow hydrograph. The model was auto calibrated and validated using SWAT-CUP SUFI2 for 10 year i.e. from 1988-1997 .The average Nash Sutcliffe efficiency (NSE) for monthly calibration and validation was found to be 0.18 and 0.70 respectively. The coefficient of determination (R²) value for monthly calibration and validation was found to be 0.31 and 0.73 and RSR value for calibration and validation was found to be 0.91 and 0.55 respectively. The study was also carried out to perform the sensitivity analysis of different parameters responsible for stream flow generation. Based on the analysis, SCS runoff curve number (CN2.mgt), Groundwater "revap" coefficient (GW_REVAP.gw), Groundwater delay days (GW_DELAY.gw), Saturated hydraulic conductivity (SOL_AWC.sol), Base flow alpha factor (ALPHA_BF.gw), Threshold depth of water in the shallow aquifer required for return flow to occur (mm) (GWQMN.gw) were found to be the most sensitive parameters. Overall the SWAT model performance was found to be satisfactory for stream flow simulation and the calibrated model can be used for runoff generation and sustainable water resource projects and development.

A comparison of performance of SWAT and machine learning models for predicting sediment load in a forested Basin, Northern Spain

In water bodies, sediment transport is a potential source of numerous negative effects on water resource projects and can damage environmental services. Two machine learning (ML) algorithms, the M5P and random forest (RF) models, have been explored for the first time as alternatives to the Soil and Water Assessment Tool (SWAT) model to estimate suspended sediment load (SSL) in the Oskotz river basin, a forested experimental basin in Navarra, northern Spain. In the ML models, streamflow and precipitation data were used to estimate daily SSL, testing different combinations of these inputs. The ML models were more accurate than the physically based hydrological SWAT model for all input scenarios tested at the daily scale. Moreover, although the SWAT results improved considerably at the monthly scale, the statistics obtained were generally inferior compared to the ML models. For the best combination of inputs, M5P demonstrated a superior ability to estimate SSL (R2 = 0.73, MAE = 135.04, RSR = 0.54, NSE = 0.71 and PBIAS = 5.19), compared to RF (R2 = 0.72, MAE = 143.39, RSR = 0.57, NSE = 0.67 and PBIAS = 11.60) and SWAT (R2 = 0.57, MAE = 181.24, RSR = 0.65, NSE = 0.57 and PBIAS = -1.27). The average sediment loads in winter, the season with the highest sediment generation in the Oskotz basin, were 2,094.04, 1,831.08 and 2,242.67 tonnes for M5P, RF and SWAT, respectively, compared to an observed SSL of 1,878.16 tonnes. These results indicate that M5P and RF are suitable models for simulating fluvial sediment production since they improved the results of the SWAT model, which also requires more time and data to set up and calibrate. However, since SWAT does not require observed streamflow as an input, it remains a useful model, achieving acceptable results in basins with limited streamflow data.

The Evaluation of the SWAT Model Performance to Predict the Runoff Values in the Iraqi Western Desert

In the present research, the Soil and Water Assessment Tool (SWAT) model was used for the prediction of surface runoff amounts of the catchment of Al-Masad, in the Western Desert of Iraq. The prediction period was from September 2020 to January 2030. The calibration and verification of this model were performed according to the daily surface runoff data that were measured between 2010 and 2014. Statistical parameters were employed to determine the performance of the model. These parameters were RSR (ratio of the root mean square error to the standard deviation of measured data), NSE (Nash-Sutcliffe efficiency), and PBias (percent Bias), which were calculated as 0.58%, 0.71%, and 13% for calibration and 0.55%, 0.74% and 11% for verification, respectively. The results from the model verification and calibration prove that this model was sufficient in simulating the catchment surface runoff. Furthermore, the SWAT model was applied for the prediction of daily, monthly, and yearly surface runoff value of the catchment from 2021 to 2030. The results obtained from the model showed that the annual surface runoff volume of the catchment, throughout the period of the simulation, was between 0.65 and 8.3 million m³ with an average value of 2.622 million m³.

Sub-basin prioritization for assessment of soil erosion susceptibility in Kangsabati, a plateau basin: A comparison between MCDM and SWAT models

Kangsabati basin located in tropical plateau region faces multiple problems of soil erosion susceptibility (SES), soil fertility deterioration, and sedimentation in reservoirs. Hence, identification of SES zones in thirty-eight sub-basins (SB) for basin prioritization is necessary. The present research addressed the issue by using four multi-criteria decision-making (MCDM) models: VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR), technique for order preference by similarity to ideal solution (TOPSIS), simple additive weighing (SAW), compound factor (CF). To determine the best fitted method from MCDM for erosion susceptibility (ES), a comparison has been made with Soil and Water Assessment Tool (SWAT), where fifteen morphometric parameters were considered for MCDM, and meteorological data, soil, slope and land use land cover (LULC) were considered for SWAT model. Two validation indices of percentage change and intensity change were used for evaluation and comparison of MCDM results. With SWAT model performance, SWAT calibration and uncertainty analysis programs (CUP) was used for sensitive analysis of SWAT parameters on flow discharge and sediment load simulation. The results showed that 23, 16, 18 SB have high ES; therefore they were given 1 to 3 ranks, whereas 31, 37, 21SB have low ES, hence given 38 to 36 rank as predicted by MCDM methods and SWAT. MCDM validation results depict that VIKOR and CF methods are more acceptable than TOPSIS and SAW. Calibration (flow discharge R2 0.86, NSE 0.75; sediment load R2 0.87, NSE 0.69) and validation (flow discharge R2 0.79, NSE 0.55; sediment load R2 0.79, NSE 0.76) of SWAT model indicated that simulated results are well fitted with observed data. Therefore, VIKOR reflects the significant role of morphometric parameters on ES, whereas SWAT reflects the significant role of LULC, slope, and soil on ES. However, it could be concluded that VIKOR is more effective MCDM method in comparison to SWAT prediction.

Evaluating SWAT model performance, considering different soils data input, to quantify actual and future runoff susceptibility in a highly urbanized basin

The Soil and Water Assessment Tool (SWAT) is a physical model designed to predict the hydrological processes that could characterize natural and anthropized watersheds. The model can be forced using input data of climate prediction models, soil characteristics and land use scenarios to forecast their effect on hydrological processes. In this study, the SWAT model has been applied in the Aspio basin, a small watershed, highly anthropized and characterized by a short runoff generation. Three simulations setup, named SL1, SL2 and SL3, were investigated using different soil resolution to identify the best model performance. An increase of space requirement and calibration time has been registered in conjunction with the increasing soil resolution. Among all simulations, SL1 has been chosen as the best one in describing watershed streamflow, despite it was characterized by the lower soil resolution. A map of susceptibility to runoff for the entire basin was so created reclassifying the runoff amount of four years in five classes of susceptibility, from very low to very high. Eleven sub-basins, coinciding with the main urban settlements, were identified as highly susceptible to runoff generation. Considering future climate predictions, a slight increase of runoff has been forecasted during summer and autumn. The map of susceptibility successfully identified as highly prone to runoff those sub-basins where extreme flood events were yet recorded in the past, remarking the reliability of the proposed assessment and suggesting that this methodology could represent a useful tool in flood managing plan.

Evaluating SWAT Model Performance for Runoff, Percolation, and Sediment Loss Estimation in Low-Gradient Watersheds of the Atlantic Coastal Plain

With predicted alterations in climate and land use, managing water resources is of the utmost importance, especially in areas such as the United States (U.S.) Coastal Plain where extensive connections exist between surface and groundwater systems. These changes create the need for models that effectively assess shifting hydrologic regimes and, in that context, we examine the performance of the Soil and Water Assessment Tool (SWAT) in a low-gradient, shallow-aquifer-dominated watershed of the U.S. Coastal Plain using a gridded reanalysis dataset. We evaluate accuracy, uncertainty, and parameter sensitivity by comparing observed and predicted streamflow at two gaging stations and assess model predictions for yearly average runoff (SURQ), percolation (PERC), and sediment loss (SYLD). Streamflow performance was acceptable during calibration (NSE = 0.67 and 0.60) and very good during validation (NSE = 0.84 and 0.91). Model predictions for SURQ, PERC, and SYLD coincided with expected ranges for this region. Parameters related to shallow aquifer properties or groundwater were highly sensitive, which indicates the need for continued study of spatial and temporal variability within the sub-surface components of these hydrologic systems. Our findings highlight the applicability of this reanalysis dataset for modeling hydrologic processes in poorly gaged watersheds and adds to the body of research that seeks to develop effective assessment tools for shallow-aquifer-dominated systems. Our methodology can effectively assist watershed managers in establishing baseline rates of hydrologic processes as is crucial with future predicted shifts in hydrologic regimes due to land-use alteration and climate change.

Calibration of a Distributed Hydrological Model in a Data-Scarce Basin Based on GLEAM Datasets

The calibration of hydrological models is often complex in regions with scarce data, and generally only uses site-based streamflow data. However, this approach will yield highly generalised values for all model parameters and hydrological processes. It is therefore necessary to obtain more spatially heterogeneous observation data (e.g., satellite-based evapotranspiration (ET)) to calibrate such hydrological models. Here, soil and water assessment tool (SWAT) models were built to evaluate the advantages of using ET data derived from the Global Land surface Evaporation Amsterdam Methodology (GLEAM) to calibrate the models for the Bayinhe River basin in northwest China, which is a typical data-scarce basin. The result revealed the following: (1) A great effort was required to calibrate the SWAT models for the study area to obtain an improved model performance. (2) The SWAT model performance for simulating the streamflow and water balance was reliable when calibrated with streamflow only, but this method of calibration grouped the hydrological processes together and caused an equifinality issue. (3) The combination of the streamflow and GLEAM-based ET data for calibrating the SWAT model improved the model performance for simulating the streamflow and water balance. However, the equifinality issue remained at the hydrologic response unit (HRU) level.

Coupling SWAT Model and CMB Method for Modeling of High-Permeability Bedrock Basins Receiving Interbasin Groundwater Flow

This paper couples the Soil and Water Assessment Tool (SWAT) model and the chloride mass balance (CMB) method to improve the modeling of streamflow in high-permeability bedrock basins receiving interbasin groundwater flow (IGF). IGF refers to the naturally occurring groundwater flow beneath a topographic divide, which indicates that baseflow simulated by standard hydrological models may be substantially less than its actual magnitude. Identification and quantification of IGF is so difficult that most hydrological models use convenient simplifications to ignore it, leaving us with minimal knowledge of strategies to quantify it. The Castril River basin (CRB) was chosen to show this problematic and to propose the CMB method to assess the magnitude of the IGF contribution to baseflow. In this headwater area, which has null groundwater exploitation, the CMB method shows that yearly IGF hardly varies and represents about 51% of mean yearly baseflow. Based on this external IGF appraisal, simulated streamflow was corrected to obtain a reduction in the percent bias of the SWAT model, from 52.29 to 22.40. Corrected simulated streamflow was used during the SWAT model calibration and validation phases. The Nash–Sutcliffe Efficiency (NSE) coefficient and the logarithmic values of NSE (lnNSE) were used for overall SWAT model performance. For calibration and validation, monthly NSE was 0.77 and 0.80, respectively, whereas daily lnNSE was 0.81 and 0.64, respectively. This methodological framework, which includes initial system conceptualization and a new formulation, provides a reproducible way to deal with similar basins, the baseflow component of which is strongly determined by IGF.

Assessing the vulnerability of water resources in the context of climate changes in a small forested watershed using SWAT: A review

The present study evaluates the applicability performance of the Soil and Water Assessment Tool (SWAT) in small forested watersheds (less than 1000 km2). This open-source software is widely used in investigations focused on water availability and quality. Overall, SWAT model performance ranges between satisfactory to good. Normally, underestimates daily peak discharges. The limitations of the model are related to the accuracy of climate data used and to the time period used for validation. Watershed area, forest cover and streamflow spatial distribution have an important influence on modeling processes. Overall, from the analyzed studies, we observed for discharge a decreasing tendency, more pronounced towards the end of the 21st century (up to −54%). For surface runoff, was noticed the same decreasing tendency up to 41%. Regarding sediment yield, the results vary within very wide limits. These findings vary according to watershed location, scenarios adopted, and the eligible period of time.